Daniel Ševčovič | Searcharxiv

arXiv2025

Structure-Preserving Coupling and Decoupling of Port-Hamiltonian Systems

The port-Hamiltonian framework is a structure-preserving modeling approach that preserves key physical properties such as energy conservation and dissipation. When subsystems are modeled as port-Hamiltonian systems (pHS) with linearly related inputs and outputs, their interconnection remains port-Hamiltonian. This paper introduces a systematic method for transforming coupled port Hamiltonian ordinary differential equations systems (pHODE) into a single monolithic formulation, and for decomposing a monolithic system into weakly coupled subsystems. The monolithic representation ensures stability and structural integrity, whereas the decoupled form enables efficient distributed simulation via operator splitting or dynamic iteration.

math.DS↗

arXiv2015

Application of the level-set model with constraints in image segmentation

We propose and analyze a constrained level-set method for semi-automatic image segmentation. Our level-set model with constraints on the level-set function enables us to specify which parts of the image lie inside respectively outside the segmented objects. Such a-priori information can be expressed in terms of upper and lower constraints prescribed for the level-set function. Constraints have the same conceptual meaning as initial seeds of the popular graph-cuts based methods for image segmentation. A numerical approximation scheme is based on the complementary-finite volumes method combined with the Projected successive over-relaxation method adopted for solving constrained linear complementarity problems. The advantage of the constrained level-set method is demonstrated on several artificial images as well as on cardiac MRI data.

math.NA↗

arXiv2014

Application of the level-set model with constraints in image segmentation

We propose and analyze a constrained level-set method for semi-automatic image segmentation. Our level-set model with constraints on the level-set function enables us to specify which parts of the image lie inside respectively outside the segmented objects. Such a-priori information can be expressed in terms of upper and lower constraints prescribed for the level-set function. Constraints have the same conceptual meaning as initial seeds of the popular graph-cuts based methods for image segmentation. A numerical approximation scheme is based on the complementary-finite volumes method combined with the Projected successive over-relaxation method adopted for solving constrained linear complementarity problems. The advantage of the constrained level-set method is demonstrated on several artificial images as well as on cardiac MRI data.

math.NA↗